William Herbert is a seasoned data scientist with over two decades of experience applying machine learning, statistical analysis, and systems engineering across the automotive and manufacturing sectors. He has led teams and projects that produced patented vehicle energy-management and safety innovations, including adaptive engine stop/start algorithms and prognostic RUL models with >96% accuracy. Comfortable with cloud and big-data stacks (Azure, AWS, GCP, Spark) and fluent in Python, SQL and C++, he bridges hands-on modeling with production-focused data engineering and dashboarding. His work often mines large CANBus and sensor datasets for practical outcomes—from hybrid battery control and fuel savings to warranty fraud detection and supply-chain optimization. A mentor and project manager as well as a coder, he has translated vehicle telematics and naturalistic driving data into deployable systems used by OEMs. Based in Birmingham, Michigan, he pairs rigorous applied math training with a knack for turning noisy, domain-specific signals into high-impact, production-ready models.
11 years of coding experience
11 years of employment as a software developer
Bachelor's degree, Mathematics, Bachelor's degree, Mathematics at Hofstra University
Master's degree, Computer Science, Master's degree, Computer Science at Oakland University
Master's degree, Applied Mathematics, Master's degree, Applied Mathematics at Adelphi University
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